CN104811621A - High-efficiency image collection and compression method - Google Patents
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Abstract
The invention relates to a high-efficiency image collection and compression method. The method includes: adopting an image detector array to acquire a quantized digital image; utilizing a lower sampling module to acquire thumbnail data of an original image; acquiring data of the original data after dimension reduction through a compression sensing module; sequentially outputting the thumbnail data and compression sensing dimension-reduction data through a compressed data output module according to a preset compression ratio to form code stream used for transmission or storage; after the compressed data are stored and transmitted, completing reconstruction of the original image through an image reconstruction module. By the high-efficiency image collection and compression method, complexity of conventional image compression is lowered effectively, and the defect that a reconstruction step is needed to realize image looking-back on the basis of conventional compression sensing data is overcome.
Description
Technical field
The present invention relates to digital image acquisition and process field, especially relate to a kind of efficient image collection and compression method.
Background technology
In order to solve the challenge of the required storage that faces and transmission when processing image mass data, we adopt compress technique usually.Namely compress technique is expressed by realizing a kind of refining to raw digital signal, reduces initial data to the restriction on memory space and transmission bandwidth.Compress technique is roughly divided into two classes: Lossless Compression and lossy compression method.Lossless Compression is as the term suggests be exactly that refining after utilizing Signal Compression is expressed and can be recovered raw digital signal without any distortion; Lossy compression method is then that the refining after utilizing Signal Compression is expressed and generally can be recovered primary signal, although signal and primary signal have certain error after recovering, error is in acceptable scope in specific applications.Clearly, but the very attractive compression ratio that can provide due to it of Lossless Compression is limited, is not thus often suitable for the occasion needing large compression ratio.Data compression technique is almost ubiquitous, and such as, the picture of our shooting, the music of listening, the video of appreciation, even in star-loaded optical remote sensing field, nearly all O-E Payload is all furnished with special data compression unit.
Transform domain coding is a kind of comparatively popular data compression method, and it transforms to primary signal in some suitable transform domains usually, digs the openness expression of the number of it is believed that in this transform domain or compressible expression-form.Here " openness expression " refers to, supposes that original signal strength is
n, this signal only has in the transform domain as illustrated
kthe coefficient of individual non-zero, wherein
k n, utilize this
kindividual nonzero coefficient can express original input signal well." compressible expression-form " refers to that primary signal can be passed through well
kthe coefficient of individual non-zero is expressed approx.Realized the compression of signal by the mode excavating signal openness expression, this compress mode adopt by many compression standards, such as JPEG, JPEG2000, H.264 with MP3 etc.Why signal can be because signal itself has very large redundancy by compression, no matter is that voice signal or picture signal are not always the case.We look back the conventional compress technique course of work, first realize the sampling of analog signal to digital signal, wherein containing a large amount of redundant datas, then excavate the openness of signal by transform domain again, realize compression finally by compression algorithm.This process contains huge waste in fact, first a large amount of redundant datas is gathered, then again these redundant datas are removed in compression process, so why not, abandon those redundant datas at the very start, the effective data of direct collection, so not only can save the cost of data acquisition, can also save space, this has just drawn the theory " compressed sensing " that this patent adopts.
The English of " compressed sensing " is expressed as Compressive sensing or Compressed sensing also or Compressive sampling, is abbreviated as CS.Simple " compression " this word, we are readily appreciated that, namely originally there being the data of redundancy to weed out, form the refined data more saving memory headroom; Simple " perception " this word is also readily appreciated that, i.e. signal sampling (analog signal becomes the process of digital signal)." compressed sensing " this blunt translation be not readily appreciated that at the beginning, but after we understand the theory of its behind, just slowly can understand its essence, namely compression and sampling are united two into one, the process that the process of namely sampling namely is compressed, the data after compressed perception sampling inherently compress after data.Compressive sensing theory points out that the signal of limited dimension that is sparse or that have sparse expression can utilize the measured value that is linear, non-self-adapting far fewer than nyquist sampling quantity to rebuild out undistortedly.For a signal
,
in only comprise
individual nonzero value.Suppose that we pass through a perception matrix
obtain
mindividual linear measurement, namely we can describe this sampling process by Mathematical Modeling below
, wherein
being a size is
matrix,
, the measured value of gained of namely sampling.Matrix
represent the projection operation of a dimensionality reduction,
be mapped to
in, in general
, i.e. matrix
columns far more than line number, this mathematical notation is namely to standard compression perception frame description.This theory, once proposition, causes extensive concern in numerous areas such as information theory, signal/image procossing, imaging of medical, radio astronomy, pattern recognition, optics/radar imagery, chnnel coding etc.
Although we are known can complete collection to echo signal and compression very efficiently by compression sensing method, but routine has to pass through reconstruction procedures based on the data that compressed sensing obtains could recover primary signal, and utilize the optimized algorithm of L1 norm minimum to need a large amount of calculating, significant discomfort closes handheld device, so mean based on the camera of compression sensing method and our are conventional uses digital camera to be accustomed to disagreing, because cannot check shooting effect by playback.And in this patent, have employed the method that down sample module and compressed sensing model combine, the hand-held user of establishing can be met and check shooting effect by thumbnail.Moreover, play the part of the role of constraints when thumbnail also utilizes L1 norm minimum to rebuild original image in this patent, the reconstructing method adding constraints so this is much better than the picture quality based on conventional compact sensing reconstructing under same compression ratio.
This has just drawn the core " a kind of efficient image collection and compression method " of this patent, this method eliminates in routine variations coding and finds important conversion coefficient and complex process to important transform coefficients encoding, thus have important practical significance: such as, for spaceborne optical imaging apparatus, omit whole compression unit to mean and save a large amount of power consumption, volume, this is concerning significant space remote sensing.This method also agrees with civil camera low-power consumption, light active demand very much simultaneously.The difficult problem all the time perplexing handheld camera or mobile phone camera manufacturer is exactly the power problems of digital camera.Such as, the peak value total power consumption of JPEG2000 compressed encoding chip ADV202 that U.S. ADI chip companies is produced can reach 0.9 watt nearly, and this is concerning the handheld device more and more popularized the present age being really a very large burden.The method that this patent proposes can reduce the power consumption of handheld device and extend its battery working time, can certainly reduce the volume and weight of handheld device.
Summary of the invention
The object of the present invention is to provide a kind of efficient image collection and compression method, to solve the problem.
In order to achieve the above object, technical scheme of the present invention is achieved in that
Efficient image gathers and compression method, it is characterized in that, comprising: image detection array, down sample module, compressed sensing module, packed data output module, image reconstruction module;
Described traditional image detection array mainly utilizes light sensitive diode (photodiode) array to carry out opto-electronic conversion;
Described down sample module realizes to a down-sampling step of original image to obtain thumbnail, and concrete down-sampling multiplying power depends on embody rule.Such as, when supposing that original image size is 2048*2048 pixel, by the thumbnail that a size is 512*512 pixel can be obtained during 16 times of down-samplings.This thumbnail meets the demand that receiving terminal or decompressor end are reviewed fast on the one hand; On the other hand, thumbnail will be incorporated into based in the solving of L1 norm minimum as constraints, makes to rebuild image result and is much better than the reconstruction quality not adopting this constraints.
Described compressed sensing module is for realizing the function of the perception matrix in compressive sensing theory, perception matrix
represent the projection operation of a dimensionality reduction,
be mapped to
in, in general
, i.e. matrix
columns far more than line number, conventional perception matrix comprises: Gaussian matrix, Bei Nuli matrix and part fourier transform matrix, but considering the embody rule that practical feasibility may limit they, Gauss's sampling matrix of such as Gaussian distributed is the feasibility almost do not realized at hardware, on the one hand, we cannot store the sampling matrix of this Gaussian Profile that obeys without questioning in memory; On the other hand, conventional memory cannot the huge sampling matrix of storage size (when we need according to a number of samples
rebuild the image that has mega pixel
time, we just need the internal memory of 10G byte, and this is very unpractical in the application of reality, and also do not consider the amount of calculation required for process of reconstruction here, so adopt completely random measurement to be very unpractiaca in the application of reality).In this patent, suggestion adopts noise waves (Noiselet) conversion to realize, but be not limited thereto, in fact every meet or approximate meet in compressive sensing theory retrain equidistant characteristics (restricted isometryproperty, RIP) and can be applied to this by hard-wired perception matrix simultaneously.Noiselet is the expression way of a kind of completely uncorrelated with wavelet transformation (Incoherence), especially when signal shows openness in Harr wavelet field, then this signal is extend in noise waves transform domain, namely meets completely in compressive sensing theory perception matrix restraint equidistant characteristics.
Described packed data output module is responsible for the output data of down sample module and compressed sensing module to carry out coding output, forms the code stream being used for transmitting or storing.This patent adopts the thumbnail data first exporting down sample module acquisition then to export the order that compressed sensing module obtains data again.If in order to improve compression ratio further, can also embed that traditional lossless compression-encoding method such as Huffman (Huffman) is encoded, count (Arithmetic coding) coding, run length encoding, self-adapting dictionary coding etc. in this order extraly.
The packed data that described image reconstruction module is responsible for receiving based on receiving terminal is to the reconstruction of original input picture.The data acquisition of this patent and compression method are obviously different from traditional nyquist sampling method, the data obtained not are through image data and are through the data after perception matrix compression, so the process must rebuild through could recover original image.Because the sampled value number obtained through this patent is obviously less than the number of pixels of original image, namely the number of unknown number is far fewer than the number of equation, so this kind of reconstruction is an ill-conditioning problem, often there is numerous solution and meets equation.Solve this kind of ill-conditioning problem, the most classical method is no more than maximum a posteriori probability (Maximum A Posteriori, MAP), but often need the prior probability model of an echo signal as a constraints, thus from numerous solution, just likely find that solution closest to target image.If add that other qualifications can also reduce solution space further, so using the result of image thumbnails as original image down-sampling in this patent, this constraints will play very important effect in the process solved by optimized algorithm.
Efficient image collection provided by the invention and compression method, compared to existing technologies, on the one hand, be used as the down-sampling thumbnail of original image as packed data; On the other hand, the compression to original image is realized by compressed sensing matrix.The advantage of this mode is: first, solve based under compressive sensing theory imaging system, immediately imaging effect cannot be reviewed, because they need a process of reconstruction based on L1 norm minimum usually, and this coded system of this patent can realize immediately reviewing imaging effect by checking the mode of thumbnail; The second, the down-sampling thumbnail of original image is used as constraints, the reconstructed image quality based on L1 norm minimum is significantly improved; 3rd, significantly reduce the complexity of IMAQ and compression.
The present invention is the packed data being then only compressed perception acquisition with prioritised transmission thumbnail at coding or transmission sequence, according to the benefit of this Sequential output be, on the one hand, first receiving terminal sees thumbnail, can select whether to be necessary super-resolution reconstruction original image; On the other hand, according to this transmission means, allow receiving terminal after receiving thumbnail under any compression ratio interrupting receive, do not affect the recovery of complete image, just in reconstructed image quality, namely difference receive more that multiple pressure contracting data reconstruction picture quality is better to some extent.
Accompanying drawing explanation
In order to be illustrated more clearly in the technical scheme in the present invention, one will be done to embodiment below to introduce simply, apparently, accompanying drawing in the following describes is one embodiment of the present of invention, for those of ordinary skill in the art, under the prerequisite not paying creative work, other accompanying drawing can also be obtained according to these accompanying drawings.
Fig. 1 shows the efficient image collection and compression method schematic diagram that the embodiment of the present invention one provides;
Reference numeral: 101-image detection array; 102-down sample module; 102-down sample module; 103-compressed sensing module; 104-packed data output module; 105-image reconstruction module.
Embodiment
For making technical scheme of the present invention and advantage clearly, below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is clearly and completely described, obviously, described embodiment is one embodiment of the invention, instead of whole embodiments.Based on the embodiment in the present invention, those of ordinary skill in the art, not making the every other embodiment obtained under creative work prerequisite, belong to the scope of protection of the invention.
Fig. 1 is the efficient image collection that provides of the embodiment of the present invention one and compression method schematic diagram, and ginseng as shown in Figure 1.The IMAQ that the embodiment of the present invention one provides and compression method comprise: image detection array 101, down sample module 102, compressed sensing module 103, packed data output module 104, image reconstruction module 105;
101: utilize light sensitive diode (photodiode) array to carry out opto-electronic conversion, such as can adopt CMOS (Complementary Metal Oxide Semiconductor) complementary metal oxide image sensor.Suppose that original input picture is after image detection array 101 carries out opto-electronic conversion, array data is lined up vector form be
;
102: realize a down-sampling step to original image, concrete down-sampling multiplying power depends on embody rule.When supposing that original image size is 2048*2048 pixel, by the thumbnail that a size is 512*512 pixel can be obtained during 16 times of down-samplings.Such as, every 4*4 block of pixels can be adopted, only retain their average or only retain the pixel in the most upper left corner.Suppose that down sample module 102 is for matrix
, then the thumbnail obtained after down sample module is expressed as by vector format equally
, then
;
103: the function realizing perception matrix in compressive sensing theory, every meet or approximate meet in compressive sensing theory retrain equidistant characteristics (restricted isometry property, and simultaneously can be applied to this by hard-wired perception matrix RIP), such as, here noise waves (Noiselet) can be adopted to convert realize, but be not limited thereto.Noiselet is a kind of and the complete incoherent expression way of wavelet transformation, especially when signal shows openness in Harr wavelet field, then this signal is extend in Noiselet transform domain, namely meet completely in compressive sensing theory perception matrix restraint equidistant characteristics (restricted isometry property, RIP).Suppose that compressed sensing module 103 is by matrix
represent, then through compressed sensing module 103 obtain packed data by vector
represent, then
;
104: carry out the output data of down sample module and compressed sensing module to encode the code stream exporting and namely formed and be used for transmitting or storing, adopt and first export the Sequential output that thumbnail data that down sample module obtains then exports the data after the compression of compressed sensing module again.Here it is to be noted, if in order to improve compression ratio further, additionally can also embed that traditional lossless compression-encoding method such as Huffman (Huffman) is encoded, count (Arithmetic coding) coding, run length encoding, self-adapting dictionary coding etc.Be that on the one hand, first receiving terminal sees thumbnail according to the benefit of this Sequential output, can select whether to be necessary super-resolution reconstruction original image; On the other hand, according to this transmission means, allow receiving terminal interrupting receive under any compression ratio after receiving thumbnail, do not affect the recovery of complete image, be in reconstructed image quality to some extent difference and the larger reconstructed image quality of compression ratio poorer.Here compression ratio is defined as:
Wherein, function
represent the byte number that data set comprises.As for when fixing compression ratio, the byte allocation of these two kinds of data sets, depends on embody rule, does not arrange in this patent.
105: the packed data received based on receiving terminal is to the reconstruction of original input picture, and the image reconstruction module of this patent can adopt following cost function to complete reconstruct:
Wherein matrix
presentation video can embody openness in certain transform domain, such as
can be wavelet transformed domain, this is because it is openness to it has been generally acknowledged that natural image can embody in wavelet field, but right
selection time, be not limited thereto, and depend on the degree of understanding to image sparse and priori.This equation solution problem above can adopt conventional threshold value to shrink iterative algorithm (IterativeShrinkage-ThresholdingAlgorithmfor, ISTA) algorithm to complete original image
reconstruct.
Claims (5)
1. IMAQ and compression method, is characterized in that, comprising: image detection array, down sample module, compressed sensing module, packed data output module, image reconstruction module;
Described image detection array is used for utilizing light sensitive diode (photodiode) array to carry out opto-electronic conversion;
Described down sample module is for obtaining the thumbnail of original image through down-sampling step;
Described compressed sensing module is for realizing the function of the perception matrix in compressive sensing theory, and the data after perception matrix sampling are exactly compressed data;
Described packed data output module is for the formation of the code stream being used for transmitting or storing;
The packed data that described image reconstruction module is used for receiving according to decoding end rebuilds original input picture.
2. IMAQ according to claim 1 and compression method, it is characterized in that, described image detection array obtains thumbnail image through down-sampling, and thumbnail is used as Partial shrinkage data passes to packed data output module, and when receiving terminal or reconstituting initial image, this thumbnail is incorporated in image reconstruction module as constraints.
3. IMAQ according to claim 1 and compression method, it is characterized in that, described compressed sensing module comprises: noise waves (Noiselet) conversion, Gaussian matrix, Bei Nuli matrix, part fourier transform matrix, other meet or approximate meet in compressive sensing theory the matrix that retrains equidistant characteristics and simultaneously can one or more in hard-wired perception matrix.
4. IMAQ according to claim 1 and compression method, is characterized in that, described packed data output module forms the code stream being used for transmitting or storing, and specifically comprises:
The order first processing the data that thumbnail data reprocessing is obtained by compressed sensing module is adopted to carry out transmitting or storing;
In order to improve compression ratio further, also can according to this order in packed data output module the lossless compression-encoding method of integrated universal comprise: Huffman (Huffman) is encoded, (Arithmetic coding) coding that counts, run length encoding, self-adapting dictionary encode in one or more.
5. IMAQ according to claim 1 and compression method, is characterized in that, described image reconstruction module adopts the optimization method based on L1 norm minimum to realize the reconstruction of original input picture;
In order to reduce solution space further, improving picture quality, in process of reconstruction, thumbnail being incorporated in the restructing algorithm of original input picture as constraints.
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| CN103186891A (en) * | 2013-03-11 | 2013-07-03 | 西安电子科技大学 | Hexagon image reconstruction method based on compressed sensing |
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